Pharmacological Research,
Journal Year:
2025,
Volume and Issue:
unknown, P. 107633 - 107633
Published: Jan. 1, 2025
There
is
an
urgent
need
for
mechanistically
novel
and
more
efficacious
treatments
schizophrenia,
especially
those
targeting
negative
cognitive
symptoms
with
a
favorable
side-effect
profile.
Drug
repurposing-the
process
of
identifying
new
therapeutic
uses
already
approved
compounds-offers
promising
approach
to
overcoming
the
lengthy,
costly,
high-risk
traditional
CNS
drug
discovery.
This
review
aims
update
our
previous
findings
on
clinical
repurposing
pipeline
in
schizophrenia.
We
examined
studies
conducted
between
2018
2024,
61
trials
evaluating
40
unique
repurposed
candidates.
These
encompassed
broad
range
pharmacological
mechanisms,
including
immunomodulation,
enhancement,
hormonal,
metabolic,
neurotransmitter
modulation.
A
notable
development
combination
muscarinic
modulators
xanomeline,
compound
antipsychotic
properties,
trospium,
included
mitigate
peripheral
side
effects,
now
by
FDA
as
first
decades
fundamentally
mechanism
action.
Moving
beyond
dopaminergic
paradigm
such
highlight
opportunities
improve
treatment-resistant
alleviate
adverse
effects.
Overall,
evolving
landscape
illustrates
significant
shift
rationale
schizophrenia
development,
highlighting
potential
silico
strategies,
biomarker-based
patient
stratification,
personalized
that
align
underlying
pathophysiological
processes.
Scientific Data,
Journal Year:
2023,
Volume and Issue:
10(1)
Published: Feb. 2, 2023
Abstract
Developing
personalized
diagnostic
strategies
and
targeted
treatments
requires
a
deep
understanding
of
disease
biology
the
ability
to
dissect
relationship
between
molecular
genetic
factors
their
phenotypic
consequences.
However,
such
knowledge
is
fragmented
across
publications,
non-standardized
repositories,
evolving
ontologies
describing
various
scales
biological
organization
genotypes
clinical
phenotypes.
Here,
we
present
PrimeKG,
multimodal
graph
for
precision
medicine
analyses.
PrimeKG
integrates
20
high-quality
resources
describe
17,080
diseases
with
4,050,249
relationships
representing
ten
major
scales,
including
disease-associated
protein
perturbations,
processes
pathways,
anatomical
entire
range
approved
drugs
therapeutic
action,
considerably
expanding
previous
efforts
in
disease-rooted
graphs.
contains
an
abundance
‘indications’,
‘contradictions’,
‘off-label
use’
drug-disease
edges
that
lack
other
graphs
can
support
AI
analyses
how
affect
networks.
We
supplement
PrimeKG’s
structure
language
descriptions
guidelines
enable
provide
instructions
continual
updates
as
new
data
become
available.
ACS Nano,
Journal Year:
2024,
Volume and Issue:
18(22), P. 14000 - 14019
Published: May 20, 2024
While
surface-enhanced
Raman
spectroscopy
(SERS)
has
experienced
substantial
advancements
since
its
discovery
in
the
1970s,
it
is
an
opportunity
to
celebrate
achievements,
consider
ongoing
endeavors,
and
anticipate
future
trajectory
of
SERS.
In
this
perspective,
we
encapsulate
latest
breakthroughs
comprehending
electromagnetic
enhancement
mechanisms
SERS,
revisit
CT
semiconductors.
We
then
summarize
strategies
improve
sensitivity,
selectivity,
reliability.
After
addressing
experimental
advancements,
comprehensively
survey
progress
on
spectrum–structure
correlation
SERS
showcasing
their
important
role
promoting
development.
Finally,
forthcoming
directions
opportunities,
especially
deepening
our
insights
into
chemical
or
biological
processes
establishing
a
clear
correlation.
Scientific Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: April 11, 2024
Abstract
Translational
research
requires
data
at
multiple
scales
of
biological
organization.
Advancements
in
sequencing
and
multi-omics
technologies
have
increased
the
availability
these
data,
but
researchers
face
significant
integration
challenges.
Knowledge
graphs
(KGs)
are
used
to
model
complex
phenomena,
methods
exist
construct
them
automatically.
However,
tackling
biomedical
problems
flexibility
way
knowledge
is
modeled.
Moreover,
existing
KG
construction
provide
robust
tooling
cost
fixed
or
limited
choices
among
representation
models.
PheKnowLator
(Phenotype
Translator)
a
semantic
ecosystem
for
automating
FAIR
(Findable,
Accessible,
Interoperable,
Reusable)
ontologically
grounded
KGs
with
fully
customizable
representation.
The
includes
resources
(e.g.,
preparation
APIs),
analysis
tools
SPARQL
endpoint
abstraction
algorithms),
benchmarks
prebuilt
KGs).
We
evaluated
by
systematically
comparing
it
open-source
analyzing
its
computational
performance
when
12
different
large-scale
KGs.
With
flexible
representation,
enables
without
compromising
usability.
Nature Medicine,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 25, 2024
Drug
repurposing-identifying
new
therapeutic
uses
for
approved
drugs-is
often
a
serendipitous
and
opportunistic
endeavour
to
expand
the
use
of
drugs
diseases.
The
clinical
utility
drug-repurposing
artificial
intelligence
(AI)
models
remains
limited
because
these
focus
narrowly
on
diseases
which
some
already
exist.
Here
we
introduce
TxGNN,
graph
foundation
model
zero-shot
drug
repurposing,
identifying
candidates
even
with
treatment
options
or
no
existing
drugs.
Trained
medical
knowledge
graph,
TxGNN
neural
network
metric
learning
module
rank
as
potential
indications
contraindications
17,080
When
benchmarked
against
8
methods,
improves
prediction
accuracy
by
49.2%
35.1%
under
stringent
evaluation.
To
facilitate
interpretation,
TxGNN's
Explainer
offers
transparent
insights
into
multi-hop
paths
that
form
predictive
rationales.
Human
evaluation
showed
predictions
explanations
perform
encouragingly
multiple
axes
performance
beyond
accuracy.
Many
align
well
off-label
prescriptions
clinicians
previously
made
in
large
healthcare
system.
are
accurate,
consistent
use,
can
be
investigated
human
experts
through
interpretable
Bioinformatics Advances,
Journal Year:
2024,
Volume and Issue:
4(1)
Published: Jan. 1, 2024
Abstract
Summary
Network
biology
is
an
interdisciplinary
field
bridging
computational
and
biological
sciences
that
has
proved
pivotal
in
advancing
the
understanding
of
cellular
functions
diseases
across
systems
scales.
Although
been
around
for
two
decades,
it
remains
nascent.
It
witnessed
rapid
evolution,
accompanied
by
emerging
challenges.
These
stem
from
various
factors,
notably
growing
complexity
volume
data
together
with
increased
diversity
types
describing
different
tiers
organization.
We
discuss
prevailing
research
directions
network
biology,
focusing
on
molecular/cellular
networks
but
also
other
such
as
biomedical
knowledge
graphs,
patient
similarity
networks,
brain
social/contact
relevant
to
disease
spread.
In
more
detail,
we
highlight
areas
inference
comparison
multimodal
integration
heterogeneous
higher-order
analysis,
machine
learning
network-based
personalized
medicine.
Following
overview
recent
breakthroughs
these
five
areas,
offer
a
perspective
future
biology.
Additionally,
scientific
communities,
educational
initiatives,
importance
fostering
within
field.
This
article
establishes
roadmap
immediate
long-term
vision
Availability
implementation
Not
applicable.
Bioinformatics,
Journal Year:
2024,
Volume and Issue:
40(7)
Published: June 24, 2024
Abstract
Motivation
Drug
repurposing
is
a
viable
solution
for
reducing
the
time
and
cost
associated
with
drug
development.
However,
thus
far,
proposed
approaches
still
need
to
meet
expectations.
Therefore,
it
crucial
offer
systematic
approach
achieve
savings
enhance
human
lives.
In
recent
years,
using
biological
network-based
methods
has
generated
promising
results.
Nevertheless,
these
have
limitations.
Primarily,
scope
of
generally
limited
concerning
size
variety
data
they
can
effectively
handle.
Another
issue
arises
from
treatment
heterogeneous
data,
which
needs
be
addressed
or
converted
into
homogeneous
leading
loss
information.
A
significant
drawback
that
most
lack
end-to-end
functionality,
necessitating
manual
implementation
expert
knowledge
in
certain
stages.
Results
We
propose
new
solution,
Heterogeneous
Graph
Transformer
Repurposing
(HGTDR),
address
challenges
repurposing.
HGTDR
three-step
graph-based
repurposing:
(1)
constructing
graph,
(2)
utilizing
graph
transformer
network,
(3)
computing
relationship
scores
fully
connected
network.
By
leveraging
HGTDR,
users
gain
ability
manipulate
input
graphs,
extract
information
diverse
entities,
obtain
their
desired
output.
evaluation
step,
we
demonstrate
performs
comparably
previous
methods.
Furthermore,
review
medical
studies
validate
our
method’s
top
10
suggestions,
exhibited
also
demonstrated
HGTDR’s
capability
predict
other
types
relations
through
numerical
experimental
validation,
such
as
drug–protein
disease–protein
inter-relations.
Availability
The
source
code
are
available
at
https://github.com/bcb-sut/HGTDR
http://git.dml.ir/BCB/HGTDR
